I am currently busy building my startup, Crawl, a nightlife marketplace and community creation platform. Previously, I helped engineer the highly reliable and low-latency APIs serving computer vision models on the Rekognition team at AWS.
I received my B.S. in Applied Mathematics and Computer Science from Tufts University in 2019 where I focused my academic efforts on dynamics, statistics, machine learning, as well as several subjects that span cognitive and computational brain sciences such as cognitive psychology, perception, and mathematical neuroscience.
In my free-time I design and build projects in the space of machine learning applications and neurotechnology. Through machine learning we have the ability to optimize so many aspects of our lives that are either tedious tasks, or that are decisions that we forfeit to a best guess.
My research interests pertain mostly to understanding how high-level cognitive behaviors manifest by means of modeling the underlying biological neural networks. Basic questions that form the foundation of this pursuit are: What underlying network dynamics form the constraints of learning? How is information coded in neural networks? Conversely, how can we effectively decode information dispersed through a network? In what ways does the connectivity among different specialized networks affect higher-level cognitive function?
We can use these questions to not only motivate progress in our understanding of the brain and behavior, but also to inspire better machine learning solutions.